Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis

TL;DR

This paper introduces new lesion-scale uncertainty measures for MRI-based multiple sclerosis lesion segmentation, demonstrating improved error detection over existing methods through an extended error retention curves framework on multi-center data.

Contribution

It proposes novel lesion-scale uncertainty measures independent of voxel-scale uncertainties and extends an evaluation framework for their assessment.

Findings

Proposed lesion-scale measure outperforms existing measures.

Extended error retention curves effectively evaluate lesion-scale uncertainty.

Code implementation is publicly available for reproducibility.

Abstract

This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI). On one side, voxel-scale segmentation errors cause the erroneous delineation of the lesions; on the other side, lesion-scale detection errors lead to wrong lesion counts. Both of these factors are clinically relevant for the assessment of multiple sclerosis patients. This work aims to compare the ability of different voxel- and lesion-scale uncertainty measures to capture errors related to segmentation and lesion detection, respectively. Our main contributions are (i) proposing new measures of lesion-scale uncertainty that do not utilise voxel-scale uncertainties; (ii) extending an error retention curves analysis framework for evaluation of lesion-scale uncertainty measures. Our results obtained on the multi-center testing set of 58 patients demonstrate that the proposed lesion-scale measure achieves the best performance among the analysed measures. All code implementations are provided at https://github.com/NataliiaMolch/MS_WML_uncs